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Open Access

A Variance Reducing Stochastic Proximal Method with Acceleration Techniques

School of Mathematical Science, Zhejiang Normal University, Jinhua 321004, China
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We consider a fundamental problem in the field of machine learning—structural risk minimization, which can be represented as the average of a large number of smooth component functions plus a simple and convex (but possibly non-smooth) function. In this paper, we propose a novel proximal variance reducing stochastic method building on the introduced Point-SAGA. Our method achieves two proximal operator calculations by combining the fast Douglas–Rachford splitting and refers to the scheme of the FISTA algorithm in the choice of momentum factors. We show that the objective function value converges to the iteration point at the rate of 𝒪(1/k) when each loss function is convex and smooth. In addition, we prove that our method achieves a linear convergence rate for strongly convex and smooth loss functions. Experiments demonstrate the effectiveness of the proposed algorithm, especially when the loss function is ill-conditioned with good acceleration.


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Tsinghua Science and Technology
Pages 999-1008
Cite this article:
Lei J, Zhang Y, Zhang Z. A Variance Reducing Stochastic Proximal Method with Acceleration Techniques. Tsinghua Science and Technology, 2023, 28(6): 999-1008.








Web of Science






Received: 01 July 2022
Revised: 13 October 2022
Accepted: 30 October 2022
Published: 28 July 2023
© The author(s) 2023.

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